Browsing by Subject "PSO"

Now showing 1 - 5 of 5
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    AI Based Modelling and Optimization of Turning Process
    (2012-08) Kulkarni, Ruturaj Jayant; El-Mounayri, Hazim; Anwar, Sohel; Wasfy, Tamer
    In this thesis, Artificial Neural Network (ANN) technique is used to model and simulate the Turning Process. Significant machining parameters (i.e. spindle speed, feed rate, and, depths of cut) and process parameters (surface roughness and cutting forces) are considered. It is shown that Multi-Layer Back Propagation Neural Network is capable to perform this particular task. Design of Experiments approach is used for efficient selection of values of parameters used during experiments to reduce cost and time for experiments. The Particle Swarm Optimization methodology is used for constrained optimization of machining parameters to minimize surface roughness as well as cutting forces. ANN and Particle Swarm Optimization, two computational intelligence techniques when combined together, provide efficient computational strategy for finding optimum solutions. The proposed method is capable of handling multiple parameter optimization problems for processes that have non-linear relationship between input and output parameters e.g. milling, drilling etc. In addition, this methodology provides reliable, fast and efficient tool that can provide suitable solution to many problems faced by manufacturing industry today.
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    Asset allocation in frequency and in 3 spatial dimensions for electronic warfare application
    (2016-04) Crespo, Jonah Greenfield; Christopher, Lauren Ann; Dos Santos, Euzeli Cipriano, Jr.; Rizkalla, Maher; Li, Lingxi; King, Brian
    This paper describes two research areas applied to Particle Swarm Optimization (PSO) in an electronic warfare asset scenario. First, a three spatial dimension solution utilizing topographical data is implemented and tested against a two dimensional solution. A three dimensional (3D) optimization increases solution space for optimization of asset location. Topography from NASA's Digital Elevation Model is also added to the solution to provide a realistic scenario. The optimization is tested for run time, average distances between receivers, average distance between receivers and paired transmitters, and transmission power. Due to load times of maps and increased iterations, the average run times were increased from 123ms to 178ms, which remains below the 1 second target for convergence speeds. The spread distance between receivers was able to increase from 86km to 89km. The distance between receiver and its paired transmitters as well as the total received power did not change signi cannily. In the second research contribution, a user input is created and placed into an unconstrained 2D active swarm. This \human in the swarm" scenario allows a user to change keep-away boundaries during optimization. The blended human and swarm solution successfully implemented human input into a running optimization with a time delay. The results of this research show that a electronic warfare solutions with real 3D topography can be simulated with minimal computational costs over two dimensional solutions and that electronic warfare solutions can successfully optimize using human input data.
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    Comparing PSO-based clustering over contextual vector embeddings to modern topic modeling
    (Elsevier, 2022-05) Miles, Samuel; Yao, Lixia; Meng, Weilin; Black, Christopher M.; Miled, Zina Ben; Electrical and Computer Engineering, School of Engineering and Technology
    Efficient topic modeling is needed to support applications that aim at identifying main themes from a collection of documents. In the present paper, a reduced vector embedding representation and particle swarm optimization (PSO) are combined to develop a topic modeling strategy that is able to identify representative themes from a large collection of documents. Documents are encoded using a reduced, contextual vector embedding from a general-purpose pre-trained language model (sBERT). A modified PSO algorithm (pPSO) that tracks particle fitness on a dimension-by-dimension basis is then applied to these embeddings to create clusters of related documents. The proposed methodology is demonstrated on two datasets. The first dataset consists of posts from the online health forum r/Cancer and the second dataset is a standard benchmark for topic modeling which consists of a collection of messages posted to 20 different news groups. When compared to the state-of-the-art generative document models (i.e., ETM and NVDM), pPSO is able to produce interpretable clusters. The results indicate that pPSO is able to capture both common topics as well as emergent topics. Moreover, the topic coherence of pPSO is comparable to that of ETM and its topic diversity is comparable to NVDM. The assignment parity of pPSO on a document completion task exceeded 90% for the 20NewsGroups dataset. This rate drops to approximately 30% when pPSO is applied to the same Skip-Gram embedding derived from a limited, corpus-specific vocabulary which is used by ETM and NVDM.
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    Nonlinear Constrained Component Optimization of a Plug-in Hybrid Electric Vehicle
    (2010-12) Yildiz, Emrah Tolga; Anwar, Sohel; Chen, Yaobin; Izadian, Afshin
    Today transportation is one of the rapidly evolving technologies in the world. With the stringent mandatory emission regulations and high fuel prices, researchers and manufacturers are ever increasingly pushed to the frontiers of research in pursuit of alternative propulsion systems. Electrically propelled vehicles are one of the most promising solutions among all the other alternatives, as far as; reliability, availability, feasibility and safety issues are concerned. However, the shortcomings of a fully electric vehicle in fulfilling all performance requirements make the electrification of the conventional engine powered vehicles in the form of a plug-in hybrid electric vehicle (PHEV) the most feasible propulsion systems. The optimal combination of the properly sized components such as internal combustion engine, electric motor, energy storage unit are crucial for the vehicle to meet the performance requirements, improve fuel efficiency, reduce emissions, and cost effectiveness. In this thesis an application of Particle Swarm Optimization (PSO) approach to optimally size the vehicle powertrain components (e.g. engine power, electric motor power, and battery energy capacity) while meeting all the critical performance requirements, such as acceleration, grade and maximum speed is studied. Compared to conventional optimization methods, PSO handles the nonlinear constrained optimization problems more efficiently and precisely. The PHEV powertrain configuration with the determined sizes of the components has been used in a new vehicle model in PSAT (Powertrain System Analysis Toolkit) platform. The simulation results show that with the optimized component sizes of the PHEV vehicle (via PSO), the performance and the fuel efficiency of the vehicle are significantly improved. The optimal solution of the component sizes found in this research increased the performance and the fuel efficiency of the vehicle. Furthermore, after reaching the desired values of the component sizes that meet all the performance requirements, the overall emission of hazardous pollutants from the PHEV powertrain is included in the optimization problem in order to obtain updated PHEV component sizes that would also meet additional design specifications and requirements.
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    OPTIMAL ENERGY MANAGEMENT SYSTEM OF PLUG-IN HYBRID ELECTRIC VEHICLE
    (ProQuest, 2009) Banvait, Harpreetsingh; Anwar, Sohel; Chen, Yaobin; Eberhart, Russell C.
    Plug-in Hybrid Electric Vehicles (PHEV) are new generation Hybrid Electric Vehicles (HEV) with larger battery capacity compared to Hybrid Electric Vehicles. They can store electrical energy from a domestic power supply and can drive the vehicle alone in Electric Vehicle (EV) mode. According to the U.S. Department of Transportation 80 % of the American driving public on average drives under 50 miles per day. A PHEV vehicle that can drive up to 50 miles by making maximum use of cheaper electrical energy from a domestic supply can significantly reduce the conventional fuel consumption. This may also help in improving the environment as PHEVs emit less harmful gases. However, the Energy Management System (EMS) of PHEVs would have to be very different from existing EMSs of HEVs. In this thesis, three different Energy Management Systems have been designed specifically for PHEVs using simulated study. For most of the EMS development mathematical vehicle models for powersplit drivetrain configuration are built and later on the results are tested on advanced vehicle modeling tools like ADVISOR or PSAT. The main objective of the study is to design EMSs to reduce fuel consumption by the vehicle. These EMSs are compared with existing EMSs which show overall improvement. x In this thesis the final EMS is designed in three intermediate steps. First, a simple rule based EMS was designed to improve the fuel economy for parametric study. Second, an optimized EMS was designed with the main objective to improve fuel economy of the vehicle. Here Particle Swarm Optimization (PSO) technique is used to obtain the optimum parameter values. This EMS has provided optimum parameters which result in optimum blended mode operation of the vehicle. Finally, to obtain optimum charge depletion and charge sustaining mode operation of the vehicle an advanced PSO EMS is designed which provides optimal results for the vehicle to operate in charge depletion and charge sustaining modes. Furthermore, to implement the developed advanced PSO EMS in real-time a possible real time implementation technique is designed using neural networks. This neural network implementation provides sub-optimal results as compared to advanced PSO EMS results but it can be implemented in real time in a vehicle. These EMSs can be used to obtain optimal results for the vehicle driving conditions such that fuel economy is improved. Moreover, the optimal designed EMS can also be implemented in real-time using the neural network procedure described.